COVID-19 population characteristics

San Francisco COVID-19 case data including age, race, sexual orientation, and homelessness status.

Race or ethnicity

COVID-19 has harmed communities of color more than other groups. This is a result of institutionalized racism and structural inequities. There is no biological or genetic difference in COVID-19 risk by race. In general, people of different races engage in the same prevention measures

Read more about health inequities and the social determinants of health.  

This dashboard shows a comparison of cases, deaths, and the San Francisco population. If all race or ethnicity groups were impacted at the same rate, the percent of cases or deaths would equal the population percentage. When a race or ethnicity group represents a higher percent of cases or deaths than the population, they are more affected.

Data notes and sources

Data notes and sources

Cases data source

Deaths data source

San Francisco population estimates are from the 2020 5-year American Community Survey.

Cases among individuals who identified as "Other" or “Multi-racial” are not shown on this dashboard. These categories do not align with the American Community Survey definitions. This means we cannot compare cases to the population. They are included in the public dataset. 

Cases missing race or ethnicity data are not shown in the dashboard. They are included in the public dataset.

We also use case rates to compare impact. Case rates are the number of cases per population. Comparing case rates across race and ethnicity groups highlights disparities.  

Case rates for smaller populations are less reliable.

Data notes and sources

Data notes and sources

Cases data source

Deaths data source

The case rate is the total cases in each race or ethnicity group divided by the number of residents in that group, and multiplied by 10,000. 

Cases among individuals who identified as "Other" or “Multi-racial” are not shown on the dashboard. These categories do not align with the American Community Survey definitions, so we cannot calculate a rate. They are included in the public dataset. 

COVID-19 is dynamic and the spread of the virus in our community may change over time. Tracking the percent of new cases by race or ethnicity every month enables us to see changes.

Data notes and sources

Data notes and sources

Cases data source

Deaths data source

Case data is shown for the most recent month once we have 15 days of data. This is to ensure that monthly estimates are reliable. Because of the five day lag, this will be on the 20th of the month. 

The most recent month may have more unknown data. The City is working to collect this data for recently reported cases. Data updates as more information becomes available. 

City Response  

Advancing racial equity is one of the City's core values. Read more on the San Francisco Office of Racial Equity’s webpage.  

There has been an enormous effort to bring resources to the communities most harmed. Many of these efforts have been community-led. The City is proud to work alongside community partners in this work. For example, we: 

  • Collaborate with the Latino Task Force 

  • Partner with community on the City’s testing strategy 

  • Support Black-owned businesses with access to financial capital and zero-interest loans 

  • Partner with community organizations on vaccine access programs 

  • Fund equity and neighborhood initiatives through our COVID Command Center  

Gender

Cisgender men account for the highest percent of cases and deaths in San Francisco. This trend has been consistent throughout the pandemic.

Data notes and sources

Data notes and sources

Cases data source

Deaths data source

We collect information on gender identity using these guidelines

Learn about California's mandate that all counties report this data. 

In the line chart showing trends over time, case data is shown for the most recent month once we have 15 days of data. This ensures that monthly estimates are reliable. Because of the five day lag, this will be on the 20th of the month. 

The most recent month may have more unknown data. Data updates as more information becomes available. 

Certain social factors that correlate with gender identity may contribute to COVID-19 risk. Learn more about this at the GenderSci Lab COVID Project.  

Tracking COVID-19 cases among transgender and gender nonconforming residents is a top priority. These residents may be particularly vulnerable because of structural inequities and other factors. We continue to work to ensure that these residents have access to the testing, resources, and support they may need. Learn more about transgender community services. 

Sexual orientation

Data notes and sources

Data notes and sources

Cases data source 

Deaths data source 

Beginning April 2020, information about sexual orientation was collected by case investigators from persons who are 18 years old or older during case investigation/contact tracing interviews or by the California Department of Public Health, Virtual Assistant. Since January 2022, information about sexual orientation has been collected only from persons aged 18+ years using the Virtual Assistant. For cases of all ages, providing sexual orientation information is voluntary. Learn more about San Francisco’s data collection guidelines.  

The totals shown in the dashboard are for cases and deaths since April 2020 when sexual orientation data collection began. Cases and deaths reported prior to April 2020, and sexual orientation information for any person < 18 years of age, are not included. 

Read more about California's mandate that all counties report this data.  

Data presented here are lagged by five days, meaning that the most recent test date included is 5 days prior to today. In addition, monthly data are displayed after at least 15 days into the month, meaning for any given month, data are available starting on the 20th of the month. Thus, the most recent month may have more missing data since information is still being collected. Data are updated as more information becomes available.

San Francisco is committed to ensuring equity across our services. Asking for sexual orientation, and gender identity (SOGI) demographic data allows us to track how well we are serving LGBTQ San Franciscans. There are many factors that contribute to public health inequities for LGBTQ communities. For example, in San Francisco, men who have sex with men bear a disproportionate burden of risk for HIV infection. Living with HIV (even when undetectable) can involve immune suppression, which is a risk factor for more severe COVID-19 symptoms. In another example, due to decades of discrimination, transgender individuals (especially those with multiple minority identities, such as trans women of color) are 18 times more likely to experience homelessness than other San Franciscans, which may compound with other known health risk factors for more severe COVID-19 symptoms. 

 

The City is committed to programs supporting LGBTQ residents.  

Read more about this in a recent study.  

Learn more about COVID-19 impacts on LGBTQ communities.  

Read more about LGBTQ community services available during the pandemic

Read more about how the State is committed to protecting the LGBTQ community during the pandemic

Experiencing homelessness

People experiencing homelessness are vulnerable to COVID-19. The number of COVID-19 cases and deaths among this group has been relatively low in San Francisco.

Data notes and sources

Data notes and sources

Cases data source

Deaths data source

Persons are identified as homeless based on several data sources: 

  • self-reported living situation  

  • the location at the time of testing 

  • Department of Public Health homelessness and health databases 

Residents in Single-Room Occupancy hotels are not included in these figures.  

These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions. 

The number of cases shown here will not equal the number of cases in the COVID-19 Alternative Housing data. Some guests of the alternative housing program are not experiencing homelessness. They may have housing but do not have the ability to safely self-isolate.  

In the chart showing trends over time, case data is shown for the most recent month once we have 15 days of data. This ensures that monthly estimates are reliable. Because of the five day lag, this will be on the 20th of the month. 

The most recent month may have more unknown data. Data updates as more information becomes available.

The City is committed to providing prevention and care services for people at risk for and experiencing homelessness. Learn more about the City’s response.   

If you are experiencing homelessness, there are resources and services available.

Age

Most San Francisco residents diagnosed with COVID-19 are between the ages of 25 and 50. The youngest age groups (those under 18) and the older age groups (over 60) have fewer cases.  

The age distribution of COVID-19 deaths is much older. Over half of deaths were among persons over the age of 80 and nearly all are over the age of 60.

Data notes and sources

Data notes and sources

Cases data source

Deaths data source

Population estimates by age group are from the National Center for Health Statistics. Learn more about their population estimates for 2020.

We also use case rates to compare impact. Case rates are the number of cases per population. Comparing case rates across age groups highlights disparities. Transitional aged youth (18-24) have some of the highest case rates in San Francisco.

Data notes and sources

Data notes and sources

We also track these trends by age over time as the situation evolves.

Data notes and sources

Data notes and sources

Cases data source

Deaths data source

In the chart showing trends over time, case data is shown for the most recent month once we have 15 days of data. This ensures that monthly estimates are reliable. Because of the five day lag, this will be on the 20th of the month. 

The most recent month may have more unknown data. Data updates as more information becomes available. 

Children under 18 make up a small percentage of all COVID-19 cases. Severe outcomes of COVID-19 among children are rare. The Department of Public Health recommends everyone get a vaccine and booster when eligible.

Data notes and sources

Data notes and sources

Cases data source  

This data incorporates a five-day lag. The date for the most recent available data is in the note under the charts.

Underlying conditions (comorbidities)

Over half of COVID-19 related deaths in San Francisco have had underlying conditions. Certain conditions are associated with an increased risk of severe illness and death. Learn more.

Data notes and sources

Data notes and sources

Cases data source

Death data source

Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death. 

In the chart showing trends over time, case data is shown for the most recent month once we have 15 days of data. This ensures that monthly estimates are reliable. Because of the five day lag, this will be on the 20th of the month. 

The most recent month may have more unknown data. Data updates as more information becomes available.

Data limitations

Data on the population characteristics of COVID-19 cases and deaths are from: 

  • Case interviews 

  • Laboratories 

  • Medical providers 

This data may not be immediately available for recently reported cases. Data updates as more information becomes available. 

Cumulative totals on this page include all cases confirmed in San Francisco since testing began in late February 2020. 

To protect resident privacy, we summarize COVID-19 data by only one characteristic at a time. Data are not shown for any subgroup with fewer than five cumulative cases. As more cases are confirmed, groups with five or more individuals will be added to the dashboards. Learn more about our privacy policy.

This data may undercount certain minorities. Residents who face stigma or discrimination in medical settings may not want to share some information. For example, stigma could result in a patient not sharing their gender identity. There are health inequities and barriers to healthcare for non-cisgender and non-heterosexual people. At this point we do not have enough data on COVID-19 to understand disparate impacts on these groups.  

The spread and severity of COVID-19 is complex. It has affected residents based on overlapping layers of structural inequities. This means that there may be intersections of populations who are particularly affected. For example, essential workers in a specific age group of a specific ethnicity. For this reason, you should interpret this data in context. Individual conclusions should be treated with caution.